---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/jesse/Dropbox/voter_id/Replication/table_a2.log
  log type:  text
 opened on:  17 Aug 2020, 14:02:35

. 
. use "$path/nc_dataset.dta" if new_reg == 0, clear

. 
. drop black white hispanic othernw new_reg noid_p_2016 dem rep

. 
. // drop 2018 
. drop if inlist(election, 11, 12)
(13,254,638 observations deleted)

. 
. sort id election

. 
. replace voted = 0 if voted == .
(0 real changes made)

. 
. // replace voted with NA if under 18 / ineligible in that election year 
. drop if birth_year > 1998 & birth_year < 9999
(70 observations deleted)

. replace voted = . if (birth_year > 1990 & birth_year < 9999) & (election == 1 | election == 2) // 2008
(1,102,090 real changes made, 1,102,090 to missing)

. replace voted = . if (birth_year > 1992 & birth_year < 9999) & (election == 3 | election == 4) // 2010
(680,176 real changes made, 680,176 to missing)

. replace voted = . if (birth_year > 1994 & birth_year < 9999) & (election == 5 | election == 6) // 2012
(269,954 real changes made, 269,954 to missing)

. replace voted = . if (birth_year > 1996 & birth_year < 9999) & (election == 7 | election == 8) // 2014
(314 real changes made, 314 to missing)

. 
. ** create string encoding of possible pre-treatment outcome paths
. tostring voted, generate(outcome_path)
outcome_path generated as str1

. 
. 
. // get pre-treatment path thorugh 2014 primary for placebo test for 2014 general
. by id: gen outcome_path_pretreat = outcome_path[1] + outcome_path[2] + ///
>                                                                 outcome_path[3] + outcome_path[4] + ///
>                                                                 outcome_path[5] + outcome_path[6] + ///
>                                                                 outcome_path[7]

. 
. 
. // keep general only
. keep if mod(election,2) == 0
(33,136,560 observations deleted)

. 
. gen count = 1

. 
. replace voted = 0 if voted == .
(1,026,267 real changes made)

. 
. // construct age bins, give young voters who are ineligible for some election their own age bin
. gen age_bin = 11 if (birth_year > 1990 & birth_year < 9999)
(30,381,335 missing values generated)

. replace age_bin = 12 if (birth_year > 1992 & birth_year < 9999)
(1,700,440 real changes made)

. replace age_bin = 13 if (birth_year > 1994 & birth_year < 9999)
(674,885 real changes made)

. replace age_bin = 14 if (birth_year > 1996 & birth_year < 9999)
(785 real changes made)

. // give those who are eligible over the whole period their own age decile
. xtile age_decile = birth_year if (birth_year <= 1990), nq(10)

. replace age_bin = age_decile if (birth_year <= 1990)
(30,334,280 real changes made)

. drop age_decile

. 
. compress
  variable count was float now byte
  variable age_bin was float now byte
  (198,819,360 bytes saved)

. 
. sort id election

. 
. // generate lead
. gen treat_lead = treat[_n+1] if id == id[_n+1]
(6,627,312 missing values generated)

. replace treat_lead = 0 if treat_lead == .
(6,627,312 real changes made)

. 
. compress
  variable treat_lead was float now byte
  (99,409,680 bytes saved)

. 
. * vanilla diff in diff
. reghdfe voted treat treat_lead, a(id election) cluster(id)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   = 33,136,560
Absorbing 2 HDFE groups                           F(   2,6627311) =   12028.13
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6024
                                                  Adj R-squared   =     0.5030
                                                  Within R-sq.    =     0.0007
Number of clusters (id)      =  6,627,312         Root MSE        =     0.3511

                             (Std. Err. adjusted for 6,627,312 clusters in id)
------------------------------------------------------------------------------
             |               Robust
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       treat |  -.1172064    .000898  -130.52   0.000    -.1189664   -.1154464
  treat_lead |   .0077208   .0008239     9.37   0.000     .0061059    .0093357
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          id |            0         6627312        6627312 *   | 
    election |            4               5              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. local b1 = _b[treat]

. local se1 = _se[treat]

. local b1_2 = _b[treat_lead]

. local se1_2 = _se[treat_lead]

. local n1 = e(N)

. local nclust1 = e(N_clust)

. 
. * race by year
. reghdfe voted treat treat_lead, a(id race_by_year) cluster(id)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   = 33,136,560
Absorbing 2 HDFE groups                           F(   2,6627311) =   12554.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6034
                                                  Adj R-squared   =     0.5042
                                                  Within R-sq.    =     0.0008
Number of clusters (id)      =  6,627,312         Root MSE        =     0.3507

                             (Std. Err. adjusted for 6,627,312 clusters in id)
------------------------------------------------------------------------------
             |               Robust
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       treat |  -.1200727    .000903  -132.97   0.000    -.1218426   -.1183028
  treat_lead |   .0088783   .0008308    10.69   0.000       .00725    .0105066
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          id |            0         6627312        6627312 *   | 
race_by_year |           24              25              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. local b2 = _b[treat]

. local se2 = _se[treat]

. local b2_2 = _b[treat_lead]

. local se2_2 = _se[treat_lead]

. local n2 = e(N)

. local nclust2 = e(N_clust)

. 
. * age by year 
. reghdfe voted treat treat_lead, a(id age_by_year) cluster(id)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   = 33,089,505
Absorbing 2 HDFE groups                           F(   2,6617900) =    8806.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6193
                                                  Adj R-squared   =     0.5241
                                                  Within R-sq.    =     0.0006
Number of clusters (id)      =  6,617,901         Root MSE        =     0.3436

                             (Std. Err. adjusted for 6,617,901 clusters in id)
------------------------------------------------------------------------------
             |               Robust
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       treat |  -.1045058    .000914  -114.34   0.000    -.1062971   -.1027144
  treat_lead |    .006295   .0008344     7.54   0.000     .0046595    .0079304
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          id |            0         6617901        6617901 *   | 
 age_by_year |          444             445              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. local b3 = _b[treat]

. local se3 = _se[treat]

. local b3_2 = _b[treat_lead]

. local se3_2 = _se[treat_lead]

. local n3 = e(N)

. local nclust3 = e(N_clust)

. 
. * age by race by year
. reghdfe voted treat treat_lead, a(id race_by_age_by_year) cluster(id)
(dropped 20 singleton observations)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   = 33,089,485
Absorbing 2 HDFE groups                           F(   2,6617896) =    8025.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6204
                                                  Adj R-squared   =     0.5255
                                                  Within R-sq.    =     0.0005
Number of clusters (id)      =  6,617,897         Root MSE        =     0.3431

                             (Std. Err. adjusted for 6,617,897 clusters in id)
------------------------------------------------------------------------------
             |               Robust
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       treat |  -.0988964   .0009212  -107.35   0.000    -.1007019   -.0970908
  treat_lead |   .0091543   .0008428    10.86   0.000     .0075025    .0108061
------------------------------------------------------------------------------

Absorbed degrees of freedom:
----------------------------------------------------------------------+
        Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
--------------------+-------------------------------------------------|
                 id |            0         6617897        6617897 *   | 
race_by_age_by_year |         2179            2180              1     | 
----------------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. local b4 = _b[treat]

. local se4 = _se[treat]

. local b4_2 = _b[treat_lead]

. local se4_2 = _se[treat_lead]

. local n4 = e(N)

. local nclust4 = e(N_clust)

. 
. ** drop 2016 general
. drop if election == 10
(6,627,312 observations deleted)

. 
. preserve 

. 
. collapse (sum) count (mean) voted, by(outcome_path_pretreat no_dmv_match election)

. 
. *** get N by path by summing together n treated and n control
. by outcome_path: gen tot_tmp = count[5] if _n==5
(1,173 missing values generated)

. egen tot_treat = sum(tot_tmp), by(outcome_path)

. by outcome_path: replace tot_tmp = count[1] if _n == 1
(171 real changes made)

. egen tot = sum(tot_tmp), by(outcome_path)

. // N_voters is the number of voters who enter into the regression
. egen N_voters = sum(tot_tmp)

. // get number of elections
. unique election
Number of unique values of election is  4
Number of records is  1336

. // N is number of voters * number of elections
. gen long N = N_voters * r(unique)

. drop tot_tmp

. gen tot_control = tot - tot_treat

. 
. *** get weights for fw based on total n per stratum
. *** each stratum has 8 obs
. *** fw requires integers so need to round
. gen tot2 = round(tot/8)

. gen tot_treat2 = round(tot_treat/8)

. egen op = group(outcome_path)

. 
. 
. reghdfe voted no_dmv_match [fw=tot_treat2], a(op election)
weight tot_treat2 can only contain strictly positive integers, but 160 zero values were found (will be dropped)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   =    218,864
Absorbing 2 HDFE groups                           F(   1, 218713) =     100.72
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.6784
                                                  Adj R-squared   =     0.6782
                                                  Within R-sq.    =     0.0005
                                                  Root MSE        =     0.2507

------------------------------------------------------------------------------
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
no_dmv_match |  -.0107566   .0010718   -10.04   0.000    -.0128573   -.0086559
-------------+----------------------------------------------------------------
    Absorbed |    F(149, 218713) =   3095.732   0.000             (Joint test)
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          op |          147             147              0     | 
    election |            3               4              1     | 
---------------------------------------------------------------+

. local b5 = _b[no_dmv_match]

. local se5 = _se[no_dmv_match]

. local n5 = N

. local nclust5 = N_voters

. 
. restore 

. preserve

. 
. collapse (sum) count (mean) voted, by(outcome_path_pretreat no_dmv_match election race_string)

. 
. *** get N by path by summing together n treated and n control
. sort outcome_path race_string no_dmv_match election

. by outcome_path race_string: gen tot_tmp = count[5] if _n==5
(5,056 missing values generated)

. egen tot_treat = sum(tot_tmp), by(outcome_path race_string)

. by outcome_path race_string: replace tot_tmp = count[1] if _n == 1
(808 real changes made)

. egen tot = sum(tot_tmp), by(outcome_path race_string)

. // N_voters is the number of voters who enter into the regression
. egen N_voters = sum(tot_tmp)

. // get number of elections
. unique election
Number of unique values of election is  4
Number of records is  5664

. // N is number of voters * number of elections
. gen long N = N_voters * r(unique)

. drop tot_tmp

. gen tot_control = tot - tot_treat

. 
. 
. *** get weights for fw based on total n per stratum
. *** each stratum has 8 obs
. *** fw requires integers so need to round
. gen tot2 = round(tot/8)

. gen tot_treat2 = round(tot_treat/8)

. egen op = group(outcome_path race_string)

. 
. reghdfe voted no_dmv_match [fw=tot_treat2], a(op election)
weight tot_treat2 can only contain strictly positive integers, but 2296 zero values were found (will be dropped)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   =    218,696
Absorbing 2 HDFE groups                           F(   1, 218271) =      90.77
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.6789
                                                  Adj R-squared   =     0.6782
                                                  Within R-sq.    =     0.0004
                                                  Root MSE        =     0.2508

------------------------------------------------------------------------------
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
no_dmv_match |  -.0102203   .0010727    -9.53   0.000    -.0123228   -.0081178
-------------+----------------------------------------------------------------
    Absorbed |    F(423, 218271) =   1090.604   0.000             (Joint test)
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          op |          421             421              0     | 
    election |            3               4              1     | 
---------------------------------------------------------------+

. local b6 = _b[no_dmv_match]

. local se6 = _se[no_dmv_match]

. qui reghdfe voted no_dmv_match [fw=tot2], a(op election)

. local nclust6 = N

. local n6 = N_voters

. 
. restore

. 
. collapse (sum) count (mean) voted, by(outcome_path_pretreat no_dmv_match election race_string age_bin)

. 
. 
. *** get N by path by summing together n treated and n control
. sort outcome_path race_string age_bin no_dmv_match election

. by outcome_path race_string age_bin: gen tot_tmp = count[5] if _n==5
(31,735 missing values generated)

. egen tot_treat = sum(tot_tmp), by(outcome_path race_string age_bin)

. by outcome_path race_string age_bin: replace tot_tmp = count[1] if _n == 1
(5,716 real changes made)

. egen tot = sum(tot_tmp), by(outcome_path race_string age_bin)

. // N_voters is the number of voters who enter into the regression
. egen N_voters = sum(tot_tmp)

. // get number of elections
. unique election
Number of unique values of election is  4
Number of records is  34692

. // N is number of voters * number of elections
. gen long N = N_voters * r(unique)

. drop tot_tmp

. gen tot_control = tot - tot_treat

. 
. *** get weights for fw based on total n per stratum
. *** each stratum has 8 obs
. *** fw requires integers so need to round
. gen tot2 = round(tot/8)

. gen tot_treat2 = round(tot_treat/8)

. egen op = group(outcome_path race_string age_bin)
(988 missing values generated)

. 
. reghdfe voted no_dmv_match [fw=tot_treat2], a(op election)
weight tot_treat2 can only contain strictly positive integers, but 22036 zero values were found (will be dropped)
(converged in 3 iterations)

HDFE Linear regression                            Number of obs   =    211,424
Absorbing 2 HDFE groups                           F(   1, 209899) =      67.74
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.6748
                                                  Adj R-squared   =     0.6724
                                                  Within R-sq.    =     0.0003
                                                  Root MSE        =     0.2528

------------------------------------------------------------------------------
       voted |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
no_dmv_match |  -.0090511   .0010997    -8.23   0.000    -.0112066   -.0068957
-------------+----------------------------------------------------------------
    Absorbed |   F(1523, 209899) =    285.929   0.000             (Joint test)
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
          op |         1521            1521              0     | 
    election |            3               4              1     | 
---------------------------------------------------------------+

. local b7 = _b[no_dmv_match]

. local se7 = _se[no_dmv_match]

. local n7 = N 

. local nclust7 = N_voters

. 
. log close
      name:  <unnamed>
       log:  /Users/jesse/Dropbox/voter_id/Replication/table_a2.log
  log type:  text
 closed on:  17 Aug 2020, 14:25:11
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
